1. The Natural Number of Forward Markets for Electricity 9 th Annual POWER Conference on Electricity Industry Restructuring March 19, 2004 Hiroaki Suenaga and Jeffrey Williams Department of Agricultural and Resource Economics University of California, Davis suenaga@primal.ucdavis.edu, williams@primal.ucdavis.edu

2. 2 Common observations about electricity: (1)Extremely volatile prices in spot wholesale markets short-run capacity constraints retail prices inflexible pronounced seasonality in demand short-run weather shocks electricity not storable (2)Underdeveloped forward wholesale markets most efforts by exchanges have failed California PX restrained to one-day-ahead generally, private bilateral trades

3. 3 Our propositions: Because of electricity’s very properties, long-dated forward markets for electricity are essentially redundant. The NYMEX natural gas futures market duplicates an electricity futures market.

4. 4 How to demonstrate that some price is redundant if it cannot be observed? An idealized world for trading electricity full profile of forward prices forward prices are best possible forecasts by construction companion forward prices for a fuel An analogy with corn considerable price variation recently well developed futures market

5. 5 Progressions of Prices of Corn Futures Contracts

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7. 6 Profiles of Corn Futures Prices in Mid June

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10. 7 Idealized Market Model (Spot Market) Generating and retailing firms trade wholesale electricity for a full constellation of delivery hours and days, far into the future. All firms are competitive and risk-neutral. Aggregate supply: P S t = b w t Q c-1 (1 +  MC e 1,t ) e 1,t =  MC e 1,t -1 + u 1,t wherew t = price of primary input (fuel) b, c,  MC,  MC = parameters u 1,t ~ iid N(0,1) Retail demand is exogenously determined (QA t ).  Equilibrium spot price in any hour: P t = b w t QA t c-1 (1 +  MC e 1,t )

11. 8 Exogenous Variables Demand (load) QA t = Q DT (t;  )(1 +  QA e 2, t )e 2,t =  QA e 2, t-1 + u 2, t Fuel Price w d = w 0, d +  w v d e 3,d e 3,d =  w e 3, d-1 + u 3, d w 0, d = w 0 (d;  ) v d = v(d;  ) u 2,t, u 3,t ~ iid N(0,1) A total of 25 parameters with 3 stochastic factors (a shock to the load, a shock to the fuel price, and a shock to the cost of generation).

12. 9 Seasonal and Diurnal Variations in Deterministic Load: Q DT t

13. 10 Seasonal cycles in fuel price and price variance

14. 11 Simulated data - 3 price relationships (1) Examine forward profiles – For each delivery hour t, generate as many forward prices, F t,t-k, as the number of k. Each is the best, unbiased forecast by construction (F t,t-k = E t-k [P t ]).  If the profiles consistently attenuate to a stable price, forward prices beyond that time ahead are redundant. (2) Examine spreads  If the spread between the forward prices of two distinct delivery hours is stable, one price can be deduced from the other. (3) Compare the forecasting ability of the forward price of primary input (w t,t-k ) with that of the forward electricity price (F t,t-k ).  If the price movements of the two commodities are highly correlated, one forward price can be deduced from the other.

15. 12 Representative time series of simulated spot prices

16. 12 Representative time series of simulated spot prices

17. 12 Representative time series of simulated spot prices

18. 12 Representative time series of simulated spot prices

19. 13 (1) Progressions of an electricity forward price

20. 13 (1) Progressions of an electricity forward price

21. 13 (1) Progressions of an electricity forward price

22. 13 (1) Progressions of an electricity forward price

23. 14 Variation across realizations in a forward price

24. 14 Variation across realizations in a forward price

25. 15 (2) Spreads among forward prices for three distinct delivery periods (Hour 18, Aug. 1, 2, and 8) - Base parameter case

26. 16 (3) Forecasting ability Regression Models (1)ln P t = a 0 + b 0 ln F t,t-k + e 0,t (2)ln P t = a 2 + b 2 ln w t,t-k + c 2 ln QF t,t-k + e 2,t Load forecast, QF t,t-k, in (2) allows the market heat rate to be non-constant and vary by season. If the R 2 for (2) is close to the R 2 for (1), the forward price of fuel predicts the spot electricity price as accurately as the electricity forward.  If so, the benefit from a separate forward market for electricity would be small.

27. 17 R-squared for regressions explaining the electricity spot price Regressor = Electricity forward

28. 17 R-squared for regressions explaining the electricity spot price - Comparison

29. 17 R-squared for regressions explaining the electricity spot price - Sensitivity

30. 18 One-Month-Ahead Forecasting Ability of Corn Futures Contracts (1996-2001)

31. 19 Six-Month-Ahead Forecasting Ability of Corn Futures Contracts (1996-2001)

32. 20 Eighteen-Month-Ahead Forecasting Ability of Corn Futures Contracts (1996-2001)

33. 21 Thirty-Month-Ahead Forecasting Ability of Corn Futures Contracts (1996-2001)

34. 22 Conclusions Forecasting ability of electricity forward prices inevitably low. Local electricity forward price profiles well represented by: Local spot markets plus forwards perhaps as far as a week ahead. Regional month-ahead energy forward market, such as natural gas. National benchmark long-dated energy forward market, such as the NYMEX natural gas. Complex varieties of contracting likely Local long-dated forward “basis” agreements.